After the pandemic, AI tutoring tool could put students back on track – EdScoop News

The coronavirus pandemic forced students and researchers at Carnegie Mellon University in March to abruptly stop testing an adaptive learning software tool that uses artificial intelligence to expand tutors ability to deliver personalized education. But researchers said the tool could help students get back up to speed on their learning when in-person instruction resumes.

The software, which was being tested in the Pittsburgh Public School District before the coronavirus outbreak began closing universities, relies on AI to identifystudents learning successes and challenges, giving educators a clear picture of how to personalize their education plans, said Lee Branstetter, professor of economics and public policy at Carnegie Mellon University.

When students work through their assignments, the AI captures everything students do,Branstetter told EdScoop. The data is then organized into a statistical map, which allows teachers to easily keep track of each students personal learning needs.

So the idea is that a tutor doesnt have to be standing behind the same student for hours to know where they are, he said. The system can help bring [educators] up to speed, but then the tutor can provide that human relationship and that accountability and that encouragement that we know is really important. Weve known since the early 1980s that personalized instruction can make a huge difference in learning outcomes, especially in students who arent necessarily the top learners in a classroom setting.

But with the learning technology of the 80s, there was no way to deliver personalized instruction at an acceptable cost.

In the decades since, artificial intelligence come a long way, Branstetter said. What were trying to do in the context of our study is to take this learning software and pair it with human tutors because an important part of the learning process is the relationship between instructors and students. We realize that software can never replicate the ability of human instructor to inspire, to encourage and to hold students accountable.

Although testing on the new tool was cut short when schools ceased in-person instruction, Branstetter said the disruption could actually be a good testing environment for the tool, and hopes toresume testing once schools reopen to help students recover lessons lost as a result of the pandemic.

I think whats almost certain to emerge is that theyre going to be students that are able to continue their education and students that are not, and the students that were already behind are going to fall further behind, he said. And so we really feel that the kind of personalized instruction that we can provide in the program will be more important and necessary than ever.

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Researchers open-source state-of-the-art object tracking AI – VentureBeat

A team of Microsoft and Huazhong University researchers this week open-sourced an AI object detector Fair Multi-Object Tracking (FairMOT) they claim outperforms state-of-the-art models on public data sets at 30 frames per second. If productized, it could benefit industries ranging from elder care to security, and perhaps be used to track the spread of illnesses like COVID-19.

As the team explains, most existing methods employ multiple models to track objects: (1) a detection model that localizes objects of interest and (2) an association model that extracts features used to reidentify briefly obscured objects. By contrast, FairMOT adopts an anchor-free approach to estimate object centers on a high-resolution feature map, which allows the reidentification features to better align with the centers. A parallel branch estimates the features used to predict the objects identities, while a backbone module fuses together the features to deal with objects of different scales.

The researchers tested FairMOT on a training data set compiled from six public corpora for human detection and search: ETH, CityPerson, CalTech, MOT17, CUHK-SYSU, and PRW. (Training took 30 hours on two Nvidia RTX 2080 graphics cards.) After removing duplicate clips, they tested the trained model against benchmarks that included 2DMOT15, MOT16, and MOT17. All came from the MOT Challenge, a framework for validating people-tracking algorithms that ships with data sets, an evaluation tool providing several metrics, and tests for tasks like surveillance and sports analysis.

Compared with the only two published works that jointly perform object detection and identity feature embedding TrackRCNN and JDE the team reports that FairMOT outperformed both on the MOT16 data set with an inference speed near video rate.

There has been remarkable progress on object detection and re-identification in recent years, which are the core components for multi-object tracking. However, little attention has been focused on accomplishing the two tasks in a single network to improve the inference speed. The initial attempts along this path ended up with degraded results mainly because the re-identification branch is not appropriately learned, concluded the researchers in a paper describing FairMOT. We find that the use of anchors in object detection and identity embedding is the main reason for the degraded results. In particular, multiple nearby anchors, which correspond to different parts of an object, may be responsible for estimating the same identity, which causes ambiguities for network training.

In addition to FairMOTs source code, the research team made available several pretrained models that can be run on live or recorded video.

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Researchers open-source state-of-the-art object tracking AI - VentureBeat

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How Hospitals Are Using AI to Battle Covid-19 – Harvard Business Review

Executive Summary

The spread of Covid-19 is stretching operational systems in health care and beyond. The reason is both simple: Our economy and health care systems are geared to handle linear, incremental demand, while the virus grows at an exponential rate. Our national health system cannot keep up with this kind of explosive demand without the rapid and large-scale adoption of digital operating models.While we race to dampen the viruss spread, we can optimize our response mechanisms, digitizing as many steps as possible. Heres how some hospitals are employing artificial intelligence to handle the surge of patients.

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On Monday March 9, in an effort to address soaring patient demand in Boston, Partners HealthCare went live with a hotline for patients, clinicians, and anyone else with questions and concerns about Covid-19. The goals are to identify and reassure the people who do not need additional care (the vast majority of callers), to direct people with less serious symptoms to relevant information and virtual care options, and to direct the smaller number of high-risk and higher-acuity patients to the most appropriate resources, including testing sites, newly created respiratory illness clinics, or in certain cases, emergency departments. As the hotline became overwhelmed, the average wait time peaked at 30 minutes. Many callers gave up before they could speak with the expert team of nurses staffing the hotline. We were missing opportunities to facilitate pre-hospital triage to get the patient to the right care setting at the right time.

The Partners team, led by Lee Schwamm, Haipeng (Mark) Zhang, and Adam Landman, began considering technology options to address the growing need for patient self-triage, including interactive voice response systems and chatbots. We connected with Providence St. Joseph Health system in Seattle, which served some of the countrys first Covid-19 patients in early March. In collaboration with Microsoft, Providence built an online screening and triage tool that could rapidly differentiate between those who might really be sick with Covid-19 and those who appear to be suffering from less threatening ailments. In its first week, Providences tool served more than 40,000 patients, delivering care at an unprecedented scale.

Our team saw potential for this type of AI-based solution and worked to make a similar tool available to our patient population. The Partners Covid-19 Screener provides a simple, straightforward chat interface, presenting patients with a series of questions based on content from the U.S. Centers for Disease Control and Prevention (CDC) and Partners HealthCare experts. In this way, it too can screen enormous numbers of people and rapidly differentiate between those who might really be sick with Covid-19 and those who are likely to be suffering from less threatening ailments. We anticipate this AI bot will alleviate high volumes of patient traffic to the hotline, and extend and stratify the systems care in ways that would have been unimaginable until recently. Development is now under way to facilitate triage of patients with symptoms to most appropriate care setting, including virtual urgent care, primary care providers, respiratory illness clinics, or the emergency department. Most importantly, the chatbot can also serve as a near instantaneous dissemination method for supporting our widely distributed providers, as we have seen the need for frequent clinical triage algorithm updates based on a rapidly changing landscape.

Similarly, at both Brigham and Womens Hospital and at Massachusetts General Hospital, physician researchers are exploring the potential use of intelligent robots developed at Boston Dynamics and MIT to deploy in Covid surge clinics and inpatient wards to perform tasks (obtaining vital signs or delivering medication) that would otherwise require human contact in an effort to mitigate disease transmission.

Several governments and hospital systems around the world have leveraged AI-powered sensors to support triage in sophisticated ways. Chinese technology company Baidu developed a no-contact infrared sensor system to quickly single out individuals with a fever, even in crowds. Beijings Qinghe railway station is equipped with this system to identify potentially contagious individuals, replacing a cumbersome manual screening process. Similarly, Floridas Tampa General Hospital deployed an AI system in collaboration with Care.ai at its entrances to intercept individuals with potential Covid-19 symptoms from visiting patients. Through cameras positioned at entrances, the technology conducts a facial thermal scan and picks up on other symptoms, including sweat and discoloration, to ward off visitors with fever.

Beyond screening, AI is being used to monitor Covid-19 symptoms, provide decision support for CT scans, and automate hospital operations. Meanwhile, Zhongnan Hospital in China uses an AI-driven CT scan interpreter that identifies Covid-19 when radiologists arent available. Chinas Wuhan Wuchang Hospital established a smart field hospital staffed largely by robots. Patient vital signs were monitored using connected thermometers and bracelet-like devices. Intelligent robots delivered medicine and food to patients, alleviating physician exposure to the virus and easing the workload of health care workers experiencing exhaustion. And in South Korea, the government released an app allowing users to self-report symptoms, alerting them if they leave a quarantine zone in order to curb the impact of super-spreaders who would otherwise go on to infect large populations.

The spread of Covid-19 is stretching operational systems in health care and beyond. We have seen shortages of everything, from masks and gloves to ventilators, and from emergency room capacity to ICU beds to the speed and reliability of internet connectivity. The reason is both simple and terrifying: Our economy and health care systems are geared to handle linear, incremental demand, while the virus grows at an exponential rate. Our national health system cannot keep up with this kind of explosive demand without the rapid and large-scale adoption of digital operating models.

While we race to dampen the viruss spread, we can optimize our response mechanisms, digitizing as many steps as possible. This is because traditional processes those that rely on people to function in the critical path of signal processing are constrained by the rate at which we can train, organize, and deploy human labor. Moreover, traditional processes deliver decreasing returns as they scale. On the other hand, digital systems can be scaled up without such constraints, at virtually infinite rates. The only theoretical bottlenecks are computing power and storage capacity and we have plenty of both. Digital systems can keep pace with exponential growth.

Importantly, AI for health care must be balanced by the appropriate level of human clinical expertise for final decision-making to ensure we are delivering high quality, safe care. In many cases, human clinical reasoning and decision making cannot be easily replaced by AI, rather AI is a decision aid that helps human improve effectiveness and efficiency.

Digital transformation in health care has been lagging other industries. Our response to Covid today has accelerated the adoption and scaling of virtual and AI tools. From the AI bots deployed by Providence and Partners HealthCare to the Smart Field Hospital in Wuhan, rapid digital transformation is being employed to tackle the exponentially growing Covid threat. We hope and anticipate that after Covid-19 settles, we will have transformed the way we deliver health care in the future.

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How Hospitals Are Using AI to Battle Covid-19 - Harvard Business Review

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When Machines Design: Artificial Intelligence and the Future of Aesthetics – ArchDaily

When Machines Design: Artificial Intelligence and the Future of Aesthetics







Are machines capable of design? Though a persistent question, it is one that increasingly accompanies discussions on architecture and the future of artificial intelligence. But what exactly is AI today? As we discover more about machine learning and generative design, we begin to see that these forms of "intelligence" extend beyond repetitive tasks and simulated operations. They've come to encompass cultural production, and in turn, design itself.

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When artificial intelligence was envisioned during thethe 1950s-60s, thegoal was to teach a computer to perform a range of cognitive tasks and operations, similar to a human mind. Fast forward half a century, andAIis shaping our aesthetic choices, with automated algorithms suggesting what we should see, read, and listen to. It helps us make aesthetic decisions when we create media, from movie trailers and music albums to product and web designs. We have already felt some of the cultural effects of AI adoption, even if we aren't aware of it.

As educator and theorist Lev Manovich has explained, computers perform endless intelligent operations. "Your smartphones keyboard gradually adapts to your typing style. Your phone may also monitor your usage of apps and adjust their work in the background to save battery. Your map app automatically calculates the fastest route, taking into account traffic conditions. There are thousands of intelligent, but not very glamorous, operations at work in phones, computers, web servers, and other parts of the IT universe."More broadly, it's useful to turn the discussion towards aesthetics and how these advancements relate to art, beauty and taste.

Usually defined as a set of "principles concerned with the nature and appreciation of beauty, aesthetics depend on who you are talking to. In 2018, Marcus Endicott described how, from the perspective of engineering, the traditional definition of aesthetics in computing could be termed "structural, such as an elegant proof, or beautiful diagram." A broader definition may include more abstract qualities of form and symmetry that "enhance pleasure and creative expression." In turn, as machine learning is gradually becoming more widely adopted, it is leading to what Marcus Endicott termed a neural aesthetic. This can be seen in recent artistic hacks, such as Deepdream, NeuralTalk, and Stylenet.

Beyond these adaptive processes, there are other ways AI shapes cultural creation. Artificial intelligence hasrecently made rapid advances in the computation of art, music, poetry, and lifestyle. Manovich explains that AIhas given us the option to automate our aesthetic choices (via recommendation engines), as well as assist in certain areas of aesthetic production such as consumer photography and automate experiences like the ads we see online. "Its use of helping to design fashion items, logos, music, TV commercials, and works in other areas of culture is already growing." But, as he concludes, human experts usually make the final decisions based on ideas and media generated by AI. And yes, the human vs. robot debate rages on.

According to The Economist, 47% of the work done by humans will have been replaced by robots by 2037, even those traditionally associated with university education. The World Economic Forum estimated that between 2015 and 2020, 7.1 million jobs will be lost around the world, as "artificial intelligence, robotics, nanotechnology and other socio-economic factors replace the need for human employees." Artificial intelligence is already changing the way architecture is practiced, whether or not we believe it may replace us. As AI is augmenting design, architects are working to explore the future of aesthetics and how we can improve the design process.

In a tech report on artificial intelligence, Building Design + Construction explored how Arup had applied a neural network to a light rail design and reduced the number of utility clashes by over 90%, saving nearly 800 hours of engineering. In the same vein, the areas of site and social research that utilize artificial intelligence have been extensively covered, and examples are generated almost daily. We know that machine-driven procedures can dramatically improve the efficiency of construction and operations, like by increasing energy performance and decreasing fabrication time and costs. The neural network application from Arup extends to this design decision-making. But the central question comes back to aesthetics and style.

Designer and Fulbright fellow Stanislas Chaillou recently created a project at Harvard utilizing machine learning to explore the future of generative design, bias and architectural style. While studying AI and its potential integration into architectural practice, Chaillou built an entire generation methodology using Generative Adversarial Neural Networks (GANs). Chaillou's project investigates the future of AI through architectural style learning, and his work illustrates the profound impact of style on the composition of floor plans.

As Chaillou summarizes, architectural styles carry implicit mechanics of space, and there are spatial consequences to choosing a given style over another. In his words, style is not an ancillary, superficial or decorative addendum; it is at the core of the composition.

Artificial intelligence and machine learningare becomingincreasingly more important as they shape our future. If machines can begin to understand and affect our perceptions of beauty, we should work to find better ways to implement these tools and processes in the design process.

Architect and researcher Valentin Soana once stated that the digital in architectural design enables new systems where architectural processes can emerge through "close collaboration between humans and machines; where technologies are used to extend capabilities and augment design and construction processes." As machines learn to design, we should work with AI to enrich our practices through aesthetic and creative ideation.More than productivity gains, we can rethink the way we live, and in turn, how to shape the built environment.

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Global Artificial Intelligence in Supply Chain Market (2020 to 2027) – by Component Technology, Application and by End User – ResearchAndMarkets.com -…

DUBLIN--(BUSINESS WIRE)--Apr 9, 2020--

The "Artificial Intelligence in Supply Chain Market by Component (Platforms, Solutions) Technology (Machine Learning, Computer Vision, Natural Language Processing), Application (Warehouse, Fleet, Inventory Management), and by End User - Global Forecast to 2027" report has been added to ResearchAndMarkets.com's offering.

This report carries out an impact analysis of the key industry drivers, restraints, challenges, and opportunities. Adoption of artificial intelligence in the supply chain allows industries to track their operations, enhance supply chain management productivity, augment business strategies, and engage with customers in the digital world.

The growth of artificial intelligence in supply chain market is driven by several factors such as raising awareness of artificial intelligence and big data & analytics and widening implementation of computer vision in both autonomous & semi-autonomous applications. Moreover, the factors such as consistent technological advancements in the supply chain industry, rising demand for AI-based business automation solutions, and evolving supply chain automation are also contributing to the market growth.

The overall AI in supply chain market is segmented by component (hardware, software, and services), by technology (machine learning, computer vision, natural language processing, cognitive computing, and context-aware computing), by application (supply chain planning, warehouse management, fleet management, virtual assistant, risk management, inventory management, and planning & logistics), and by end-user (manufacturing, food and beverages, healthcare, automotive, aerospace, retail, and consumer-packaged goods), and geography.

Companies Mentioned

Key Topics Covered:

1. Introduction

2. Research Methodology

3. Executive Summary

3.1. Overview

3.2. Market Analysis, by Component

3.3. Market Analysis, by Technology

3.4. Market Analysis, by Application

3.5. Market Analysis, by End User

3.6. Market Analysis, by Geography

3.7. Competitive Analysis

4. Market Insights

4.1. Introduction

4.2. Market Dynamics

4.2.1. Drivers Rising Awareness of Artificial Intelligence and Big Data & Analytics Widening Implementation of Computer Vision in both Autonomous & Semi-Autonomous Applications

4.2.2. Restraints High Procurement and Operating Cost Lack of Infrastructure

4.2.3. Opportunities Growing Demand for AI -Based Business Automation Solutions Evolving Supply Chain Automation

4.2.4. Challenges Data Integration from Multiple Resources Concerns Over Data Privacy

4.2.5. Trends Rising Adoption of 5g Technology Rising Demand for Cloud-Based Supply Chain Solutions

5. Artificial Intelligence in Supply Chain Market, by Component

5.1. Introduction

5.2. Software

5.2.1. AI Platforms

5.2.2. AI Solutions

5.3. Services

5.3.1. Deployment & Integration

5.3.2. Support & Maintenance

5.4. Hardware

5.4.1. Networking

5.4.2. Memory

5.4.3. Processors

6. Artificial Intelligence in Supply Chain Market, by Technology

6.1. Introduction

6.2. Machine Learning

6.3. Natural Language Processing (NLP)

6.4. Computer Vision

6.5. Context-Aware Computing

7. Artificial Intelligence in Supply Chain Market, by Application

7.1. Introduction

7.2. Supply Chain Planning

7.3. Virtual Assistant

7.4. Risk Management

7.5. Inventory Management

7.6. Warehouse Management

7.7. Fleet Management

7.8. Planning & Logistics

8. Artificial Intelligence in Supply Chain Market, by End User

8.1. Introduction

8.2. Retail Sector

8.3. Manufacturing Sector

8.4. Automotive Sector

8.5. Aerospace Sector

8.6. Food & Beverage Sector

8.7. Consumer Packaged Goods Sector

8.8. Healthcare Sector

9. Global Artificial Intelligence in Supply Chain Market, by Geography

9.1. Introduction

9.2. North America

9.2.1. U.S.

9.2.2. Canada

9.3. Europe

9.3.1. Germany

9.3.2. U.K.

9.3.3. France

9.3.4. Spain

9.3.5. Italy

9.3.6. Rest of Europe

9.4. Asia-Pacific

9.4.1. China

9.4.2. Japan

9.4.3. India

9.4.4. Rest of Asia-Pacific

9.5. Latin America

9.6. Middle East & Africa

10. Competitive Landscape

10.1. Key Growth Strategies

10.2. Competitive Developments

10.2.1. New Product Launches and Upgradations

10.2.2. Mergers and Acquisitions

10.2.3. Partnerships, Agreements, & Collaborations

10.2.4. Expansions

10.3. Market Share Analysis

10.4. Competitive Benchmarking

11. Company Profiles (Business Overview, Financial Overview, Product Portfolio, Strategic Developments)

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Spending in Artificial Intelligence to accelerate across the public sector due to automation and social distancing compliance needs in response to…

April 9, 2020 - LONDON, UK: Prior to the COVID-19 pandemic, the IDC (International Data Corporation) Worldwide Artificial Intelligence Spending Guide had forecast European artificial intelligence (AI) spending of $10 billion for 2020, and a healthy growth at a 33% CAGR throughout 2023. With the COVID-19 outbreak, IDC expects a variety of changes in spending in 2020. AI solutions deployed in the cloud will experience a strong uptake, showing that companies are looking at deploying intelligence in the cloud to be more efficient and agile.

"Following the COVID-19 outbreak, many industries such as transportation and personal and consumer services will be forced to revise their technology investments downwards," said Andrea Minonne, senior research analyst at IDC Customer Insights & Analysis. "On the other hand, AI is a technology that can play a significant role in helping businesses and societies deal with and solve large scale disruption caused by quarantines and lockdowns. Of all industries, the public sector will experience an acceleration of AI investments. Hospitals are looking at AI to speed up COVID-19 diagnosis and testing and to provide automated remote consultations to patients in self-isolation through chatbots. At the same time, governments will use AI to assess social distancing compliance"

In the IDC report, What is the Impact of COVID-19 on the European IT Market? (IDC #EUR146175020, April 2020) we assessed the impact of COVID-19 across 181 European companies and found that, as of March 23, 16% of European companies believe automation through AI and other emerging technologies can help them minimize the impact of COVID-19. With large scale lockdowns in place, a shortage of workers and supply chain disruptions will drive automation needs across manufacturing.

Applying intelligence to automate processes is a crucial response to the COVID-19 crisis. Not only does automation allow European companies to digitally transform, but also to make prompt data-driven decisions and have a positive impact on business efficiency. IDC expects a surge in adoption of automated COVID-19 diagnosis in healthcare to speed up diagnosis and save time for both doctors and patients. As the virus spreads quickly, labor shortages in industries where product demand is surging can become a critical problem. For that reason, companies are renovating their hiring processes, applying a mix of intelligent automation and virtualization in their hiring processes. Companies will also aim to automate their supply chains, maintain their agility and avoid production bottlenecks, especially for industries with vast supplier networks. With customer service centers becoming severely restricted, automation will be a crucial part for remote customer engagement and chatbots will help customers in self-isolation get the support they need without having to wait a long time.

"As a short-term response to the COVID-19 crisis, AI can play a crucial part in automating processes and limiting human involvement to a necessary minimum," said Petr Vojtisek, research analyst at IDC Customer Insights & Analysis. "In the longer term, we might observe an increase in AI adoption for companies that otherwise wouldn't consider it, both for competitive and practical reasons."

IDC's Worldwide Semiannual Artificial Intelligence Spending Guide provides guidance on the expected technology opportunity around the AI market across nine regions. Segmented by 32 countries, 19 industries, 27 use cases, and 6 technologies, the guide provides IT vendors with insight into this rapidly growing market and how the market will develop over the coming years.

For IDCs European coverage of COVID-19, click here.

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Microsofts CTO explains how AI can help health care in the US right now – The Verge

This week for our Vergecast interview series, Verge editor-in-chief Nilay Patel chats with Microsoft chief technology officer Kevin Scott about his new book Reprogramming the American Dream: From Rural America to Silicon ValleyMaking AI Serve Us All.

Scotts book tackles how artificial intelligence and machine learning can help rural America in a more grounding way, from employment to education to public health. In one chapter of his book, Scott focuses on how AI can assist with health care and diagnostic issues a prominent concern in the US today, especially during the COVID-19 pandemic.

In the interview, Scott refocuses the solutions he describes in the book around the current crisis, specifically supercomputers Microsoft has been using to train natural language processing now being used to search for vaccine targets and therapies for the novel coronavirus.

Below is a lightly edited excerpt of the conversation.

So lets talk about health care because its something you do focus on in the book. Its a particularly poignant time to talk about health care. How do you see AI helping broadly with health care and then more specifically with the current crisis?

I think there are a couple of things going on.

One I think is a trend that I wrote about in the book and that is just getting more obvious every day is that we need to do more. So that particular thing is that if our objective as a society is to get higher-quality, lower-cost health care to every human being who needs it, I think the only way that you can accomplish all three of those goals simultaneously is if you use some form of technological disruption.

And I think AI can be exactly that thing. And youre already seeing an enormous amount of progress on the AI-powered diagnostics front. And just going into the crisis that were in right now, one of the interesting things that a bunch of folks are doing including, I think I read a story about the Chan Zuckerberg Initiative is doing this is the idea is that if you have ubiquitous biometric sensing, like youve got a smartwatch or a fitness band or maybe something even more complicated that can sort of read off your heart-tick data, that can look at your body temperature, that can measure the oxygen saturation in your blood, that can basically get a biometric readout of how your bodys performing. And its sort of capturing that information over time. We can build diagnostic models that can look at those data and determine whether or not youre about to get sick and sort of predict with reasonable accuracy whats going on and what you should do about it.

Like you cant have a cardiologist following you around all day long. There arent enough cardiologists in the world even to give you a good cardiological exam at your annual checkup.

I think this isnt a far-fetched thing. There is a path forward here for deploying this stuff on a broader scale. And it will absolutely lower the cost of health care and help make it more widely available. So thats one bucket of things. The other bucket of things is like just some mind-blowing science that gets enabled when you intersect AI with the leading-edge stuff that people are doing in the biosciences.

Give me an example.

So, two things that we have done relatively recently at Microsoft.

One is one of the big problems in biology that weve had that that immunologists have been studying for years and years and years, is whether or not you could take a readout of your immune system by looking at the distribution of the types of T-cells that are active in your body. And from that profile, determine what illnesses that your body may be actively dealing with. What is it prepared to deal with? Like what might you have recently had?

And that has been a hard problem to figure out because, basically, youre trying to build something called a T-cell receptor antigen map. And now, with our sequencing technology, we have the ability to get the profile so you can sort of see what your immune system is doing. But we have not yet figured out how to build that mapping of the immune system profile to diseases.

Except were partnering with this company called Adaptive that is doing really great work with us, like bolting machine learning onto this problem to try to figure out what the mapping actually looks like. We are rushing right now a serologic test like a blood test that we hope well be able to sort of tell you whether or not you have had a COVID-19 infection.

So I think its mostly going to be useful for understanding the sort of spread of the disease. I dont think its going to be as good a diagnostic test as like a nasal swab and one of the sequence-based tests that are getting pushed out there. But its really interesting. And the implications are not just for COVID-19, but if you are able to better understand that immune system profile, the therapeutic benefits of that are just absolutely enormous. Weve been trying to figure this out for decades.

The other thing that were doing is when youre thinking about SARS-CoV-2 which is the virus that causes COVID-19 that is raging through the world right now we have never in human history had a better understanding of a virus and how it is attacking the body. And weve never had a better set of tools for precision engineering, potential therapies, and vaccines for this thing. And part of that engineering process is using a combination of simulation and machine learning and these cutting-edge techniques of biosciences in a way where youre sort of leveraging all three at the same time.

So weve got this work that were doing with a partner right now where I have taken a set of supercomputing clusters that we have been using to train natural language processing, deep neural networks, just massive scale. And those clusters are now being used to search for vaccine targets and therapies for SARS-CoV-2.

Were one among a huge number of people who are very quickly searching for both therapies and potential vaccines. There are reasons to be hopeful, but weve got a way to go.

But its just unbelievable to me to see how these techniques are coming together. And one of the things that Im hopeful about as we deal with this current crisis and think about what we might be able to do on the other side of it is it could very well be that this is the thing that triggers a revolution in the biological sciences and investment in innovation that has the same sort of a decades-long effect that the industrialization push around World War II had in the 40s that basically built our entire modern world.

Yeah, thats what I keep coming back to, this idea that this is a reset on a scale that very few people living today have ever experienced.

And you said out of World War II, a lot of basic technology was invented, deployed, refined. And now we kind of get to layer in things like AI in a way that is, quite frankly, remarkable. I do think, I mean, it sounds like were going to have to accept that Cortana might be a little worse at natural language processing while you search for the protein surfaces. But I think its a trade most people make.

[Laughs] I think thats the right trade-off.

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The Untapped Potential of Conversational AI: Content in Context – CMSWire

PHOTO:Jose Morales

I am a baseball fan. A totally over-the-top baseball fan. This will come as no surprise to anyone who has followed me for any length of time.

(At this point I'd like to say to those people who are not interested in baseball and artificial intelligence could there be such a person? stick with me here.)

This year, I was recruited to be a part of a fantasy league. For those who know about such things, its a 5x5 league (Batting = SB, R, RBI, Avg, HR and Pitching = W, Saves, WHIP, ERA, Strikeouts). I was honored to be invited because this particular league has been around since before baseball statistics became so ubiquitous. It goes back to the time when fantasy baseball league commissioners needed to await the arrival of USA Today each week, and manually input tedious statistics into a spreadsheet.

Well, those days are obviously gone. This particular league is called the NOVA Braggin' Rights Fantasy Challenge and is housed on CBSSports.com. The integration that CBSSports has done to automate the process of scouting, team drafting, and league administration is mind-boggling in its own right. We had our league draft on March 15 before everything hit the fan re: the postponement of the season. The draft took four hours, which my wife found incredibly humorous.

(Before I go any further, for my baseball scouting credibility, let me say that there was a method to my madness in my draft selection. On the hitting side, I decided to emphasize speed, thereby hopefully optimizing the SB, Average, and Runs categories and on the pitching side, I was focused on Wins, WHIP and Strikeouts.)

Still here? Let's move from baseball nerdiness to AI nerdiness.

Related Article: 3 Ways AI Helps Content Teams Work Faster, Smarter, Better

About 10 minutes after we completed our marathon draft, I got an incredibly detailed personal email highlighting my successes and failures in the draft. This is just a small portion. (Note: The cryptic names mentioned in the email are the other teams in the league.)

Your Draft Grade: C

With the draft now over, the 2020 fantasy baseball season has officially begun, and no team has gotten off to a better start than Rainman Cometh. Rolling with the best player in baseball worked out, as Coach Willis' squad are projected to wind up with 94 category points. That's 47 more points than The Holmbres are projected to come up with. Despite drafting a (supposedly healthy) former NL MVP in Christian Yelich, we're projecting that Coach Holmlund will wind up at the back of the pack.

You managed to find yourself in the middle of the pack with the 9th best draft overall. You might have been among the best in the league if it weren't for your outfielders, who are projected to be the 4th worst in the league. But at least you are better at that position than Tom's Legends, who are even worse. Coach Needham will have to trot out Alex Verdugo, Jo Adell, and Dylan Carlson into the starting lineup. Flintstones2 will have no such difficulty when it comes to outfielders, pacing the league with players like Cody Bellinger, Charlie Blackmon, and Ketel Marte. Their ability to put together that good group is a little less impressive given that they had the 3rd easiest path through the draft.

Speaking of draft difficulty, you had it pretty rough, as you ended up with less value available to you than all but one other team. You had to watch as good value picks like Carlos Santana and Josh Donaldson were snatched right before it was your turn.

Looking at individual picks, we thought Kershawshank Redemption made the best move by drafting Gary Sanchez in the 124th slot. He was projected to be off the board a full 69 picks earlier. In the bad picks department, nobody made a worse move than The Thrill. Coach Adleberg surprised everybody by choosing Kolten Wong with the 84th pick, which we pegged as a serious reach.

Your best pickup of the draft was Danny Santana, who we thought should have been selected around the 68th slot, but who you got with pick #114. Not all of your picks were superb, however, as you also selected Daniel Hudson, whose projections suggested that he should have gone undrafted.

What is amazing about this from a customer experience perspective is the incredible amount of personalization and detail incorporated into this email. I will put aside for a moment my "C" rating. Even more amazing is that this email arrived only 10 minutes after we finished the draft. And in another step up the CX ladder, note the very human conversational style. And you guessed it no human was involved in the creation of this email.

Related Article: Will Artificial Intelligence Write Performance Evaluations One Day?

Ive always been intrigued by how AI can be used to automatically create conversational documents based on data think annual reports and short sports articles and wire services reports. Many of these are now written by AI. But this one seemed particularly nuanced.

I noticed the name of the company behind the email in fine print infoSentience. It describes its core value as the ability to process huge volumes of data and deliver on-demand, high-quality narratives.

I asked its CEO, Steve Wasick, whether the Gartners and Forresters of the world have recognized this area as a unique technology space and given it a name. As far as I know, they haven't. We like to think of our technology as an 'analyst in a box' so I wouldn't be surprised if they try to use technology like ours in the future. We really think this technology has applications within almost any field. Any industry that has too much data to analyze and report on manually could use our help. We actually have products now in finance, medicine, and defense.

It strikes me that this technology's strength in adding to the customer experience is its ability to truly personalize the interaction. According to Wasick, Giving people general information is nice, but they are obviously going to respond much better to information that is unique to them. Most companies just don't realize that it is even an option to personalize many of the bulk communications that they are currently sending out.

Of course, as someone who makes a living basically stringing words together, I had to ask him about the long-term impact on journalism. It's not likely to replace anything that people are currently writing. Instead, our technology is able to allow for reporting in situations where it wouldn't be economical to have human writers. Not quite sure I agree with that last point, especially for those kinds of writing gigs that are usually handed off to aspiring recent journalism majors, but OK.

Related Article: Content Marketing Strategy: Context, Context, Context

My core point in all of this is that the next frontier of content in context something weve spent a lot of time talking about in the content management space is to automate combining data and content into a seamless and conversational communication. And conversations that are not stilted and contrived, but that meet the Turing Test.

Just FYI, this article was written by a real human being. Or was it?

John Mancini is the President of Content Results, LLC and the Past President of AIIM. He is a well-known author, speaker, and advisor on information management, digital transformation and intelligent automation.

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The Top 100 AI Startups Out There Now, and What They’re Working On – Singularity Hub

New drug therapies for a range of chronic diseases. Defenses against various cyber attacks. Technologies to make cities work smarter. Weather and wildfire forecasts that boost safety and reduce risk. And commercial efforts to monetize so-called deepfakes.

What do all these disparate efforts have in common? Theyre some of the solutions that the worlds most promising artificial intelligence startups are pursuing.

Data research firm CB Insights released its much-anticipated fourth annual list of the top 100 AI startups earlier this month. The New York-based company has become one of the go-to sources for emerging technology trends, especially in the startup scene.

About 10 years ago, it developed its own algorithm to assess the health of private companies using publicly-available information and non-traditional signals (think social media sentiment, for example) thanks to more than $1 million in grants from the National Science Foundation.

It uses that algorithm-generated data from what it calls a companys Mosaic scorepulling together information on market trends, money, and momentumalong with other details ranging from patent activity to the latest news analysis to identify the best of the best.

Our final list of companies is a mix of startups at various stages of R&D and product commercialization, said Deepashri Varadharajanis, a lead analyst at CB Insights, during a recent presentation on the most prominent trends among the 2020 AI 100 startups.

About 10 companies on the list are among the worlds most valuable AI startups. For instance, theres San Francisco-based Faire, which has raised at least $266 million since it was founded just three years ago. The company offers a wholesale marketplace that uses machine learning to match local retailers with goods that are predicted to sell well in their specific location.

Another startup valued at more than $1 billion, referred to as a unicorn in venture capital speak, is Butterfly Network, a company on the East Coast that has figured out a way to turn a smartphone phone into an ultrasound machine. Backed by $350 million in private investments, Butterfly Network uses AI to power the platforms diagnostics. A more modestly funded San Francisco startup called Eko is doing something similar for stethoscopes.

In fact, there are more than a dozen AI healthcare startups on this years AI 100 list, representing the most companies of any industry on the list. In total, investors poured about $4 billion into AI healthcare startups last year, according to CB Insights, out of a record $26.6 billion raised by all private AI companies in 2019. Since 2014, more than 4,300 AI startups in 80 countries have raised about $83 billion.

One of the most intensive areas remains drug discovery, where companies unleash algorithms to screen potential drug candidates at an unprecedented speed and breadth that was impossible just a few years ago. It has led to the discovery of a new antibiotic to fight superbugs. Theres even a chance AI could help fight the coronavirus pandemic.

There are several AI drug discovery startups among the AI 100: San Francisco-based Atomwise claims its deep convolutional neural network, AtomNet, screens more than 100 million compounds each day. Cyclica is an AI drug discovery company in Toronto that just announced it would apply its platform to identify and develop novel cannabinoid-inspired drugs for neuropsychiatric conditions such as bipolar disorder and anxiety.

And then theres OWKIN out of New York City, a startup that uses a type of machine learning called federated learning. Backed by Google, the companys AI platform helps train algorithms without sharing the necessary patient data required to provide the sort of valuable insights researchers need for designing new drugs or even selecting the right populations for clinical trials.

Privacy and data security are the focus of a number of AI cybersecurity startups, as hackers attempt to leverage artificial intelligence to launch sophisticated attacks while also trying to fool the AI-powered systems rapidly coming online.

I think this is an interesting field because its a bit of a cat and mouse game, noted Varadharajanis. As your cyber defenses get smarter, your cyber attacks get even smarter, and so its a constant game of whos going to match the other in terms of tech capabilities.

Few AI cybersecurity startups match Silicon Valley-based SentinelOne in terms of private capital. The company has raised more than $400 million, with a valuation of $1.1 billion following a $200 million Series E earlier this year. The companys platform automates whats called endpoint security, referring to laptops, phones, and other devices at the end of a centralized network.

Fellow AI 100 cybersecurity companies include Blue Hexagon, which protects the edge of the network against malware, and Abnormal Security, which stops targeted email attacks, both out of San Francisco. Just down the coast in Los Angeles is Obsidian Security, a startup offering cybersecurity for cloud services.

Deepfakes of videos and other types of AI-manipulated media where faces or voices are synthesized in order to fool viewers or listeners has been a different type of ongoing cybersecurity risk. However, some firms are swapping malicious intent for benign marketing and entertainment purposes.

Now anyone can be a supermodel thanks to Superpersonal, a London-based AI startup that has figured out a way to seamlessly swap a users face onto a fashionista modeling the latest threads on the catwalk. The most obvious use case is for shoppers to see how they will look in a particular outfit before taking the plunge on a plunging neckline.

Another British company called Synthesia helps users create videos where a talking head will deliver a customized speech or even talk in a different language. The startups claim to fame was releasing a campaign video for the NGO Malaria Must Die showing soccer star David Becham speak in nine different languages.

Theres also a Seattle-based company, Wellsaid Labs, which uses AI to produce voice-over narration where users can choose from a library of digital voices with human pitch, emphasis, and intonation. Because every narrator sounds just a little bit smarter with a British accent.

Speaking of smarter: A handful of AI 100 startups are helping create the smart city of the future, where a digital web of sensors, devices, and cloud-based analytics ensure that nobody is ever stuck in traffic again or without an umbrella at the wrong time. At least thats the dream.

A couple of them are directly connected to Google subsidiary Sidewalk Labs, which focuses on tech solutions to improve urban design. A company called Replica was spun out just last year. Its sort of SimCity for urban planning. The San Francisco startup uses location data from mobile phones to understand how people behave and travel throughout a typical day in the city. Those insights can then help city governments, for example, make better decisions about infrastructure development.

Denver-area startup AMP Robotics gets into the nitty gritty details of recycling by training robots on how to recycle trash, since humans have largely failed to do the job. The U.S. Environmental Protection Agency estimates that only about 30 percent of waste is recycled.

Some people might complain that weather forecasters dont even do that well when trying to predict the weather. An Israeli AI startup, ClimaCell, claims it can forecast rain block by block. While the company taps the usual satellite and ground-based sources to create weather models, it has developed algorithms to analyze how precipitation and other conditions affect signals in cellular networks. By analyzing changes in microwave signals between cellular towers, the platform can predict the type and intensity of the precipitation down to street level.

And those are just some of the highlights of what some of the worlds most promising AI startups are doing.

You have companies optimizing mining operations, warehouse logistics, insurance, workflows, and even working on bringing AI solutions to designing printed circuit boards, Varadharajanis said. So a lot of creative ways in which companies are applying AI to solve different issues in different industries.

Image Credit: Butterfly Network

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COVID-19: AI can help – but the right human input is key – World Economic Forum

Artificial intelligence (AI) has the potential to help us tackle the pressing issues raised by the COVID-19 pandemic. It is not the technology itself, though, that will make the difference but rather the knowledge and creativity of the humans who use it.

Indeed, the COVID-19 crisis will likely expose some of the key shortfalls of AI. Machine learning, the current form of AI, works by identifying patterns in historical training data. When used wisely, AI has the potential to exceed humans not only through speed but also by detecting patterns in that training data that humans have overlooked.

However, AI systems need a lot of data, with relevant examples in that data, in order to find these patterns. Machine learning also implicitly assumes that conditions today are the same as the conditions represented in the training data. In other words, AI systems implicitly assume that what has worked in the past will still work in the future.

A new strain of Coronavirus, COVID 19, is spreading around the world, causing deaths and major disruption to the global economy.

Responding to this crisis requires global cooperation among governments, international organizations and the business community, which is at the centre of the World Economic Forums mission as the International Organization for Public-Private Cooperation.

The Forum has created the COVID Action Platform, a global platform to convene the business community for collective action, protect peoples livelihoods and facilitate business continuity, and mobilize support for the COVID-19 response. The platform is created with the support of the World Health Organization and is open to all businesses and industry groups, as well as other stakeholders, aiming to integrate and inform joint action.

As an organization, the Forum has a track record of supporting efforts to contain epidemics. In 2017, at our Annual Meeting, the Coalition for Epidemic Preparedness Innovations (CEPI) was launched bringing together experts from government, business, health, academia and civil society to accelerate the development of vaccines. CEPI is currently supporting the race to develop a vaccine against this strand of the coronavirus.

What does this have to do with the current crisis? We are facing unprecedented times. Our situation is jarringly different from that of just a few weeks ago. Some of what we need to try today will have never been tried before. Similarly, what has worked in the past may very well not work today.

Humans are not that different from AI in these limitations, which partly explains why our current situation is so daunting. Without previous examples to draw on, we cannot know for sure the best course of action. Our traditional assumptions about cause and effect may no longer hold true.

Humans have an advantage over AI, though. We are able to learn lessons from one setting and apply them to novel situations, drawing on our abstract knowledge to make best guesses on what might work or what might happen. AI systems, in contrast, have to learn from scratch whenever the setting or task changes even slightly.

The COVID-19 crisis, therefore, will highlight something that has always been true about AI: it is a tool, and the value of its use in any situation is determined by the humans who design it and use it. In the current crisis, human action and innovation will be particularly critical in leveraging the power of what AI can do.

One approach to the novel situation problem is to gather new training data under current conditions. For both human decision-makers and AI systems alike, each new piece of information about our current situation is particularly valuable in informing our decisions going forward. The more effective we are at sharing information, the more quickly our situation is no longer novel and we can begin to see a path forward.

Projects such as the COVID-19 Open Research Dataset, which provides the text of over 24,000 research papers, the COVID-net open-access neural network, which is working to collaboratively develop a system to identify COVID-19 in lung scans, and an initiative asking individuals to donate their anonymized data, represent important efforts by humans to pool data so that AI systems can then sift through this information to identify patterns.

Global spread of COVID-19

Image: World Economic Forum

A second approach is to use human knowledge and creativity to undertake the abstraction that the AI systems cannot do. Humans can discern between places where algorithms are likely to fail and situations in which historical training data is likely still relevant to address critical and timely issues, at least until more current data becomes available.

Such systems might include algorithms that predict the spread of the virus using data from previous pandemics or tools that help job seekers identify opportunities that match their skillsets. Even though the particular nature of COVID-19 is unique and many of the fundamental rules of the labour market are not operating, it is still possible to identify valuable, although perhaps carefully circumscribed, avenues for applying AI tools.

Efforts to leverage AI tools in the time of COVID-19 will be most effective when they involve the input and collaboration of humans in several different roles. The data scientists who code AI systems play an important role because they know what AI can do and, just as importantly, what it cant. We also need domain experts who understand the nature of the problem and can identify where past training data might still be relevant today. Finally, we need out-of-the-box thinkers who push us to move beyond our assumptions and can see surprising connections.

Toronto-based startup Bluedot is an example of such a collaboration. In December it was one of the first to identify the emergence of a new outbreak in China. Its system relies on the vision of its founder, who believed that predicting outbreaks was possible, and combines the power several different AI tools with the knowledge of epidemiologists who identified where and how to look for evidence of emerging diseases. These epidemiologists also verify the results at the end.

Reinventing the rules is different from breaking the rules, though. As we work to address our current needs, we must also keep our eye on the long-term consequences. All of the humans involved in developing AI systems need to maintain ethical standards and consider possible unintended consequences of the technologies they create. While our current crisis is very pressing, we cannot sacrifice our fundamental principles to address it.

The key takeaway is this: Despite the hype, there are many ways that humans in which still surpass the capabilities of AI. The stunning advances that AI has made in recent years are not an inherent quality of the technology, but rather a testament to the humans who have been incredibly creative in how they use a tool that is mathematically and computationally complex and yet at its foundation still quite simple and limited.

As we seek to move rapidly to address our current problems, therefore, we need to continue to draw on this human creativity from all corners, not just the technology experts but also those with knowledge of the settings, as well as those who challenge our assumptions and see new connections. It is this human collaboration that will enable AI to be the powerful tool for good that it has the potential to be.

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World Economic Forum articles may be republished in accordance with our Terms of Use.

Written by

Matissa Hollister, Assistant Professor of Organizational Behaviour, McGill University

The views expressed in this article are those of the author alone and not the World Economic Forum.

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Enlisting AI in our war on coronavirus: Potential and pitfalls | TheHill – The Hill

Given the outsized hold Artificial Intelligence (AI) technology has acquired on public imagination of late, it comes as no surprise that many are wondering whatAI can do for the public health crisis wrought by the COVID-19 coronavirus.

A casual search of AI and COVID-19 already returns a plethora of news stories, many of them speculative. While AI technology is not ready to help with the magical discovery of a new vaccine, there are important ways it can assist in this fight.

Controlling epidemics is, in large part, based on laborious contact tracing and using that information to predict the spread. We live in a time in which we constantly leave digital footprints through our daily life and interactions. These massive troves of data can be analyzed with AI technologies for detection, contact tracing and to find infection clusters, spread patterns and identify high-risk patients.

There is some evidence that AI techniques analyzing news feeds and social media data were not too far behind humans in originally detecting the COVID-19 outbreak in Wuhan. China seems not only to have used existing digital traces but also enforced additional ones; for example, citizens in Nanjing are required to register their presence in subway trains and many shops by scanning QR codes with their cellphones. Singapore, lauded for its effective containment of the virus without widespread lockdowns, has used public cameras to trace the interaction patterns of the infected, and even introduced a crowd-sourced app for voluntary contact tracing. In the U.S., research efforts are underway to mine body temperature and heart-rate data from wearables for early detection of COVID-19 infection.

It is widely feared that COVID-19 cases, at their peak, will overwhelm medical infrastructure in many cities. Evidence from Hubei, China, and Lombardy, Italy, do indeed support this fear. One way to alleviate this situation is to adopt novel methods of remotely providing medical help. The basic infrastructure for telemedicine has existed for a long time but has been a hard-sell from consumer, provider and regulatory points of view until now. Already, the U.S. has waived regulations to allow doctors to practice across state boundaries; the Department of Health and Human Services (HHS) has also announced that it will not levy penalties on medical providers using certain virtual communication tools, such as Skype and FaceTime, to connect with patients.

AI technologies certainly can help as a force-multiplier here, as front-line medical decision support tools for patient-provider matching, triage and even in faster diagnosis. For example, the Chinese company Alibaba claims rapid diagnostic image analytics for chest CT scans; China also has leveraged robots in disinfection of public spaces. Remote tele-presence robots increasingly could be leveraged to bring virtual movement and solace to people in forced medical quarantines.

AI technologies already have been enablers of, and defenders against, fake news. In the context of this pandemic, our incomplete knowledge coupled with angst has led to an infodemic of unreliable/fake information about coping with the outbreak, often spread by well-meaning (if gullible) people. AI technologies certainly can be of help here, both in flagging stories of questionable lineage and pointing to more trusted information sources.

AI also can be used to distill COVID-19-related information. A prominent example here is a White House Office of Science and Technology Policy-supported effort to use natural language-processing technologies to mine the stream of research papers relevant to the COVID-19 virus, with the aim of helping scientists quickly gain insight and spot trends within the research. There is some hope that such distillation can help in vaccine discovery efforts, too.

Suppression by social distancing has emerged as the most promising way to stem the tide of infection. It is clear, however, that social distancing like sticking to a healthy diet runs very much counter to our natural impulses. Short of draconian state enforcement, what can we do to increase the chances that people follow the best practices? One way AI can help here is via micro-targeted behavioral nudges. Like it or not, AI technologies already harvest vast troves of user profiles via our digital footprints and weaponize those for targeted ads. The same technologies can be readily rejiggered for subliminal micro-targeted social distancing messages that could include distracting us from cabin fever. There already is some evidence that mild nudging can even reduce the sharing of misinformation.

Lockdowns and social distancing measures are affecting the education of millions of schoolchildren. Tutoring services assisted by AI technologies can help significantly when students are stuck at home. China reportedly has relied on the help of online AI-based tutoring companies such as Squirrel AI to engage some of its millions of schoolchildren in lockdown.

And while self-driving cars remain a distant dream, delivering essential goods to people via deserted streets certainly could be within reach. Depending on how long shelter-at-home continues, we might rely increasingly on such technologies to transport critical personnel and goods.

Some of these potential uses of AI are controversial, as they infringe on privacy and civil liberties or reflect the very type of applications that the AI ethics community has resisted. Do we really want our personal AI assistants to start nudging us subliminally? Should we support increased cellphone tracking for infection control? It will be interesting to see to what extent society is willing to adopt them.

Indeed, our readiness to try almost anything to fight this unprecedented viral war is opening an inadvertent window into how we might handle the worries surrounding an AI-enabled future. Ideas such as universal basic income (UBI) in the presence of widespread technological unemployment, or concerns about diminished privacy thanks to widespread AI-based surveillance all are coming to the fore.

China has mobilized state resources to feed its quarantined population and used extensive cellphone tracking to analyze the spread of the virus. Israel is reportedly using cellphone tracking to ensure quarantines, as did Taiwan. The U.S. is considering UBI-like ideas e.g., providing thousand-dollar checks to many adults effectively unemployed during the pandemic and is reportedly mulling cellphone-based tracking to get people to follow social distancing guidelines.

Once such practices are adopted, they will no longer just be theoretical constructs. Some or all of them will become part of our society beyond this war on the virus, just as many Great Depression-era programs became part of our social fabric. The possibility that our choices in this time of crisis can change our society in crucial ways is raising alarms and calls for circumspection. Yet, to what extent civil society is likely to pause for circumspection at the height of this execution imperative remains to be seen.

Subbarao Kambhampati, PhD, is a professor of computer science at Arizona State University and chief AI officer for AI Foundation, which focuses on the responsible development of AI technologies. He served as president and is now past-president of the Association for the Advancement of Artificial Intelligence and was a founding board member of Partnership on AI. He can be followed on Twitter@rao2z.

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Microsoft teams up with leading universities to tackle coronavirus pandemic using AI – TechRepublic

The newly-formed C3.ai Digital Transformation Institute has an open call for proposals to mitigate the COVID-19 epidemic using artificial intelligence and machine learning.

With the coronavirus impacting most of the world, the medical community is hard at work trying to come up with some type of magic bullet that will stop the pandemic from propagating. Can artificial intelligence (AI) and machine learning (ML) help nurture a solution? That's what Microsoft and a host of top universities are hoping.

In a blog post published last week, Microsoft detailed the creation of the C3.ai Digital Transformation Institute (C3.ai DTI), a consortium of scientists, researchers, innovators, and executives from the academic and corporate worlds whose mission it is to push AI to achieve social and economic benefits. As such, C3.ai DTI will sponsor and fund scientists and researchers to spur the digital transformation of business, government, and society.

Created by Microsoft, AI software provider C3.ai, and several leading universities, C3.ai DTI already has the first task on its agenda--to harness the power of AI to combat the coronavirus.

SEE:Coronavirus: Critical IT policies and tools every business needs(TechRepublic Premium)

Known as "AI Techniques to Mitigate Pandemic," C3.ai DTI's first call for research proposals is asking scholars, developers, and researchers to "embrace the challenge of abating COVID-19 and advance the knowledge, science, and technologies for mitigating future pandemics using AI." Researchers are free to develop their own topics in response to this subject, but the consortium outlined 10 different areas open for consideration:

"We are collecting a massive amount of data about MERS, SARS, and now COVID-19," Condoleezza Rice, former US Secretary of State, said in the blog post. "We have a unique opportunity before us to apply the new sciences of AI and digital transformation to learn from these data how we can better manage these phenomena and avert the worst outcomes for humanity."

This first call is currently open with a deadline of May 1, 2020. Interested participants can check the C3.ai DTI website to learn about the process and find out how to submit their proposals. Selected proposals will be announced by June 1, 2020.

The group will fund as much as $5.8 million in awards for this first call, with cash awards ranging from $100,000 to $500,000 each. Recipients will also receive cloud computing, supercomputing, data access, and AI software resources and technical support provided by Microsoft and C3.ai. Specifically, those with successful proposals will get unlimited use of the C3 AI Suite, access to the Microsoft Azure cloud platform, and access to the Blue Waters supercomputer at the National Center for Super Computing Applicationsat the University of Illinois Urbana-Champaign (UIUC).

To fund the institute, C3.ai will provide $57,250,000 over the first five years of operation. C3.ai and Microsoft will contribute an additional $310 million, which includes use of the C3 AI Suite and Microsoft Azure. The universities involved in the consortium include the UIUC; the University of California, Berkeley; Princeton University; the University of Chicago; the Massachusetts Institute of Technology; and Carnegie Mellon University.

Beyond funding successful research proposals, Microsoft said that C3.ai DTI will generate new ideas for the use of AI and ML through ongoing research, visiting professors and research scholars, and faculty and scholars in residence, many of whom will come from the member universities.

More specifically, the group will focus its research on AI, ML, Internet of Things, Big Data Analytics, human factors, organizational behavior, ethics, and public policy. This research will examine new business models, develop ways for creating change within organizations, analyze methods to protect privacy, and ramp up the conversations around the ethics and public policy of AI.

"In these difficult times, we need--now more than ever--to join our forces with scholars, innovators, and industry experts to propose solutions to complex problems," Gwenalle Avice-Huet, Executive Vice President of ENGIE, said. "I am convinced that digital, data science, and AI are a key answer. The C3.ai Digital Transformation Institute is a perfect example of what we can do together to make the world better."

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Google is using AI to design chips that will accelerate AI – MIT Technology Review

A new reinforcement-learning algorithm has learned to optimize the placement of components on a computer chip to make it more efficient and less power-hungry.

3D Tetris: Chip placement, also known as chip floor planning, is a complex three-dimensional design problem. It requires the careful configuration of hundreds, sometimes thousands, of components across multiple layers in a constrained area. Traditionally, engineers will manually design configurations that minimize the amount of wire used between components as a proxy for efficiency. They then use electronic design automation software to simulate and verify their performance, which can take up to 30 hours for a single floor plan.

Time lag: Because of the time investment put into each chip design, chips are traditionally supposed to last between two and five years. But as machine-learning algorithms have rapidly advanced, the need for new chip architectures has also accelerated. In recent years, several algorithms for optimizing chip floor planning have sought to speed up the design process, but theyve been limited in their ability to optimize across multiple goals, including the chips power draw, computational performance, and area.

Intelligent design: In response to these challenges, Google researchers Anna Goldie and Azalia Mirhoseini took a new approach: reinforcement learning. Reinforcement-learning algorithms use positive and negative feedback to learn complicated tasks. So the researchers designed whats known as a reward function to punish and reward the algorithm according to the performance of its designs. The algorithm then produced tens to hundreds of thousands of new designs, each within a fraction of a second, and evaluated them using the reward function. Over time, it converged on a final strategy for placing chip components in an optimal way.

Validation: After checking the designs with the electronic design automation software, the researchers found that many of the algorithms floor plans performed better than those designed by human engineers. It also taught its human counterparts some new tricks, the researchers said.

Production line: Throughout the field's history, progress in AI has been tightly interlinked with progress in chip design. The hope is this algorithm will speed up the chip design process and lead to a new generation of improved architectures, in turn accelerating AI advancement.

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Researchers find AI is bad at predicting GPA, grit, eviction, job training, layoffs, and material hardship – VentureBeat

A paper coauthored by over 112 researchers across 160 data and social science teams found that AI and statistical models, when used to predict six life outcomes for children, parents, and households, werent very accurate even when trained on 13,000 data points from over 4,000 families. They assert that the work is a cautionary tale on the use of predictive modeling, especially in the criminal justice system and social support programs.

Heres a setting where we have hundreds of participants and a rich data set, and even the best AI results are still not accurate, said study co-lead author Matt Salganik, a professor of sociology at Princeton and interim director of the Center for Information Technology Policy at the Woodrow Wilson School of Public and International Affairs. These results show us that machine learning isnt magic; there are clearly other factors at play when it comes to predicting the life course.

The study, which was published this week in the journal Proceedings of the National Academy of Sciences, is the fruit of the Fragile Families Challenge, a multi-year collaboration that sought to recruit researchers to complete a predictive task by predicting the same outcomes using the same data. Over 457 groups applied, of which 160 were selected to participate, and their predictions were evaluated with an error metric that assessed their ability to predict held-out data (i.e., data held by the organizer and not available to the participants).

The Challenge was an outgrowth of the Fragile Families Study (formerly Fragile Families and Child Wellbeing Study) based at Princeton, Columbia University, and the University of Michigan, which has been studying a cohort of about 5,000 children born in 20 large American cities between 1998 and 2000. Its designed to oversample births to unmarried couples in those cities, and to address four questions of interest to researchers and policymakers:

When we began, I really didnt know what a mass collaboration was, but I knew it would be a good idea to introduce our data to a new group of researchers: data scientists, said Sara McLanahan, the William S. Tod Professor of Sociology and Public Affairs at Princeton. The results were eye-opening.

The Fragile Families Study data set consists of modules, each of which is made up of roughly 10 sections, where each section includes questions about a topic asked of the childrens parents, caregivers, teachers, and the children themselves. For example, a mother who recently gave birth might be asked about relationships with extended kin, government programs, and marriage attitudes, while a 9-year-old child might be asked about parental supervision, sibling relationships, and school. In addition to the surveys, the corpus contains the results of in-home assessments, including psychometric testing, biometric measurements, and observations of neighborhoods and homes.

The goal of the Challenge was to predict the social outcomes of children aged 15 years, which encompasses 1,617 variables. From the variables, six were selected to be the focus:

Contributing researchers were provided anonymized background data from 4,242 families and 12,942 variables about each family, as well as training data incorporating the six outcomes for half of the families. Once the Challenge was completed, all 160 submissions were scored using the holdout data.

In the end, even the best of the over 3,000 models submitted which often used complex AI methods and had access to thousands of predictor variables werent spot on. In fact, they were only marginally better than linear regression and logistic regression, which dont rely on any form of machine learning.

Either luck plays a major role in peoples lives, or our theories as social scientists are missing some important variable, added McLanahan. Its too early at this point to know for sure.

Measured by the coefficient of determination, or the correlation of the best models predictions with the ground truth data, material hardship i.e., whether 15-year-old childrens parents suffered financial issues was .23, or 23% accuracy. GPA predictions were 0.19 (19%), while grit, eviction, job training, and layoffs were 0.06 (6%), 0.05 (5%), and 0.03 (3%), respectively.

The results raise questions about the relative performance of complex machine-learning models compared with simple benchmark models. In the Challenge, the simple benchmark model with only a few predictors was only slightly worse than the most accurate submission, and it actually outperformed many of the submissions, concluded the studys coauthors. Therefore, before using complex predictive models, we recommend that policymakers determine whether the achievable level of predictive accuracy is appropriate for the setting where the predictions will be used, whether complex models are more accurate than simple models or domain experts in their setting, and whether possible improvement in predictive performance is worth the additional costs to create, test, and understand the more complex model.

The research team is currently applying for grants to continue studies in this area, and theyve also published 12 of the teams results in a special issue of a journal called Socius, a new open-access journal from the American Sociological Association. In order to support additional research, all the submissions to the Challenge including the code, predictions, and narrative explanations will be made publicly available.

The Challenge isnt the first to expose the predictive shortcomings of AI and machine learning models. The Partnership on AI, a nonprofit coalition committed to the responsible use of AI, concluded in its first-ever report last year that algorithms are unfit to automate the pre-trial bail process or label some people as high-risk and detain them. The use of algorithms in decision making for judges has been known to produce race-based unfair results that are more likely to label African-American inmates as at risk of recidivism.

Its well-understood that AI has a bias problem. For instance, word embedding, a common algorithmic training technique that involves linking words to vectors, unavoidably picks up and at worst amplifies prejudices implicit in source text and dialogue. A recent study by the National Institute of Standards and Technology (NIST) found that many facial recognition systems misidentify people of color more often than Caucasian faces. And Amazons internal recruitment tool which was trained on resumes submitted over a 10-year period was reportedly scrapped because it showed bias against women.

A number of solutions have been proposed, from algorithmic tools to services that detect bias by crowdsourcing large training data sets.

In June 2019, working with experts in AI fairness, Microsoft revised and expanded the data sets it uses to train Face API, a Microsoft Azure API that provides algorithms for detecting, recognizing, and analyzing human faces in images. Last May, Facebook announced Fairness Flow, which automatically sends a warning if an algorithm is making an unfair judgment about a person based on their race, gender, or age. Google recently released the What-If Tool, a bias-detecting feature of the TensorBoard web dashboard for its TensorFlow machine learning framework. Not to be outdone, IBM last fall released AI Fairness 360, a cloud-based, fully automated suite that continually provides [insights] into how AI systems are making their decisions and recommends adjustments such as algorithmic tweaks or counterbalancing data that might lessen the impact of prejudice.

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Researchers find AI is bad at predicting GPA, grit, eviction, job training, layoffs, and material hardship - VentureBeat

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Google Researchers Create AI-ception with an AI Chip That Speeds Up AI – Interesting Engineering

Reinforcement learning algorithms may be the next best thing since sliced bread for engineers looking to improve chip placement.

Researchers from Google have created a new algorithm that has learned how to optimize the placement of the components in a computer chip, so as to make it more efficient and less power-hungry.


Typically, engineers can spend up to 30 hours configuring a single floor plan of chip placement, or chip floor planning. This complicated 3D design problem requires the configuration of hundreds, or even thousands, of components across a number of layers in a constrained area. Engineers will manually design configurations to minimize the number of wires used between components as a proxy for efficiency.

Because this is time-consuming, these chips are designed to only last between two and five years. However, as machine-learning algorithms keep improving year upon year, a need for new chip architectures has also arisen.

Facing these challenges, Google researchers Anna Goldie and Azalia Mirhoseini, have looked into reinforcement learning. These types of algorithms use positive and negative feedback in order to learn new and complicated tasks. Thus, the algorithm is either "rewarded" or "punished" depending on how well it learns a task. Following this, it then creates tens to hundreds of thousands of new designs. Ultimately, it creates an optimal strategy on how to place these chip components.

After their tests, the researchers checked their designs with the electronic design automation software and discovered that their method's floor planning was much more effective than the ones human engineers designed. Moreover, the system was able to teach its human workers a new trick or two.

Progress in AI has been largely interlinked with progress is computer chip design. The researchers' hope is that their new algorithm will assist in speeding up the chip design process and pave the way for new and improved architectures, which would ultimately accelerate AI.

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iFLYTEK and Hancom Group Launch Accufly.AI to Help Combat the Coronavirus Pandemic – Business Wire

HEFEI, China--(BUSINESS WIRE)--Asias leading artificial intelligence (AI) and speech technology company, iFLYTEK has partnered with the South Korean technology company, Hancom Group, to launch the joint venture Accufly.AI in South Korea. Accufly.AI launched its AI Outbound Calling System to assist the South Korean government at no cost and provide information to individuals who have been in close contact with or have had a confirmed coronavirus case.

The AI Outbound Calling System is a smart, integrated system that is based on iFLYTEK solutions and Hancom Groups Korean-based speech recognition. The technology saves manpower and assists in the automatic distribution of important information to potential carriers of the virus and provides a mechanism for follow up with recovered patients. iFLYTEK is looking to make this technology available in markets around the world, including North American and Europe.

The battle against the Covid-19 epidemic requires collective wisdom and sharing of best practices from the international community, said iFLYTEK Chief Financial Officer Mr. Dawei Duan. Given the challenges we all face, iFLYTEK is continuously looking at ways to provide technologies and support to partners around the world, including in the United States, Canada, the United Kingdom, New Zealand, and Australia.

In February, the Hancom Group donated 20,000 protective masks and 5 thermal devices to check temperatures to Anhui to help fight the epidemic.

iFLYTEKs AI technology helped stem the spread of the virus in China and will help the South Korean government conduct follow-up, identify patients with symptoms, manage self-isolated residents, and reduce the risk of cross-infection. The system also will help the government distribute important health updates, increase public awareness, and bring communities together.

iFLYTEK is working to create a better world through artificial intelligence and seeks to do so on a global scale. iFLYTEK will maximize its technical advantages in smart services to support the international community in defeating the coronavirus, said Mr. Duan.


iFLYTEK and Hancom Group Launch Accufly.AI to Help Combat the Coronavirus Pandemic - Business Wire

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A.I. Versus the Coronavirus – The New York Times

Advanced computers have defeated chess masters and learned how to pick through mountains of data to recognize faces and voices. Now, a billionaire developer of software and artificial intelligence is teaming up with top universities and companies to see if A.I. can help curb the current and future pandemics.

Thomas M. Siebel, founder and chief executive of C3.ai, an artificial intelligence company in Redwood City, Calif., said the public-private consortium would spend $367 million in its initial five years, aiming its first awards at finding ways to slow the new coronavirus that is sweeping the globe.

I cannot imagine a more important use of A.I., Mr. Siebel said in an interview.

Known as the C3.ai Digital Transformation Institute, the new research consortium includes commitments from Princeton, Carnegie Mellon, the Massachusetts Institute of Technology, the University of California, the University of Illinois and the University of Chicago, as well as C3.ai and Microsoft. It seeks to put top scientists onto gargantuan social problems with the help of A.I. its first challenge being the pandemic.

The new institute will seek new ways of slowing the pathogens spread, speeding the development of medical treatments, designing and repurposing drugs, planning clinical trials, predicting the diseases evolution, judging the value of interventions, improving public health strategies and finding better ways in the future to fight infectious outbreaks.

Condoleezza Rice, a former U.S. secretary of state who serves on the C3.ai board and was recently named the next director of the Hoover Institution, a conservative think tank on the Stanford campus, called the initiative a unique opportunity to better manage these phenomena and avert the worst outcomes for humanity.

The new institute plans to award up to 26 grants annually, each featuring up to $500,000 in research funds in addition to computing resources. It requires the principal investigators to be located at the consortiums universities but allows partners and team members at other institutions. It wants coronavirus proposals to be submitted by May and plans to award its first grants in June. The research findings are to be made public.

The institutes co-directors are S. Shankar Sastry of the University of California, Berkeley, and Rayadurgam Srikant of the University of Illinois, Urbana-Champaign. The computing power is to come from C3.ai and Microsoft, as well as the Lawrence Berkeley National Laboratory at the University of California and the National Center for Supercomputing Applications at the University of Illinois. The schools run some of the worlds most advanced supercomputers.

Successful A.I. can be extremely hard to deliver, especially in thorny real-world problems such as self-driving cars. When asked if the institute was less a plan for practical results than a feel-good exercise, Mr. Siebel replied, The probability of something good not coming out of this is zero.

In recent decades, many rich Americans have sought to reinvent themselves as patrons of social progress through science research, in some cases outdoing what the federal government can achieve because its goals are often unadventurous and its budgets unpredictable.

Forbes puts Mr. Siebels current net worth at $3.6 billion. His First Virtual Group is a diversified holding company that includes philanthropic ventures.

Born in 1952, Mr. Siebel studied history and computer science at the University of Illinois and was an executive at Oracle before founding Siebel Systems in 1993. It pioneered customer service software and merged with Oracle in 2006. He founded what came to be named C3.ai in 2009.

The first part of the companys name, Mr. Siebel said in an email, stands for the convergence of three digital trends: big data, cloud computing and the internet of things, with A.I. amplifying their power. Last year, he laid out his thesis in a book Digital Transformation: Survive and Thrive in an Era of Mass Extinction. C3.ai works with clients on projects like ferreting out digital fraud and building smart cities.

In an interview, Eric Horvitz, the chief scientist of Microsoft and a medical doctor who serves on the spinoff institutes board, likened the push for coronavirus solutions to a compressed moon shot.

The power of the approach, he said, comes from bringing together key players and institutions. We forget who is where and ask what we can do as a team, Dr. Horvitz said.

Seeing artificial intelligence as a good thing perhaps a lifesaver is a sharp reversal from how it often gets held in dread. Critics have assailed A.I. as dangerously powerful, even threatening the enslavement of humanity to robots with superhuman powers.

In no way am I suggesting that A.I. is all sweetness and light, Mr. Siebel said. But the new institute, he added, is a place where it can be a force for good.

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NEC and Kagome to Provide AI-enabled Services That Improve Tomato Yields – Business Wire

TOKYO--(BUSINESS WIRE)--NEC Corporation today announced the conclusion of a strategic partnership agreement with Kagome Co., Ltd. to launch agricultural management support services utilizing AI for leading tomato processing companies.

The new service uses NECs AI-enabled agricultural ICT platform, CropScope, to visualize tomato growth and soil conditions based on sensor data and satellite images, and to provide farming management recommendation services. This AI enables the service to provide data on the best timing and amounts of irrigation and fertilizer for healthy crops. As a result, farms are able to achieve stable yields and lower costs, while practicing environmentally sustainable agriculture without depending on the skill of individual growers.

Tomato processing companies can obtain a comprehensive understanding of the most effective growing conditions for tomato production on their own farms, as well as their contract growers. Also, they can optimally manage crop harvest orders across all fields based on objective data, which helps to reduce yield loss and improve productivity.

NEC and Kagome began agricultural collaboration in 2015, and by 2019 they had conducted demonstrations in regions that include Portugal, Australia and the USA. An AI farming experiment in Portugal in 2019 showed that the amount of fertilizer used for the trial was approximately 20% less than the average amount used in general, yielding 127 tons of tomatoes per hectare, approximately 1.3 times that of the average Portuguese grower, and almost the same as that of skilled growers.

Kagome will establish a Smart Agri Division in April 2020, first targeting customers in Europe, then aiming to expand the business to worldwide markets.

Kagome has been developing agricultural management support technologies using big data in collaboration with NEC since 2015, with the aim of realizing environmentally friendly and highly profitable agricultural management in the cultivation of tomatoes for processing on a global basis, said Kengo Nakata, General Manager, Smart Agri Division, Kagome. By combining Kagomes farming know-how with NEC's AI technology, we will realize sustainable agriculture, he added.

NEC is pleased to have signed a strategic partnership agreement with Kagome, said Masamitsu Kitase, General Manager, Corporate Business Development Division, NEC. NEC aims to realize a sustainable agriculture that can respond flexibly to global social issues on climate change and food safety, he added.

About NEC Corporation: For more information, visit NEC at http://www.nec.com.

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behold.ai and Wellbeing Software collaborate on national solution for rapid COVID-19 diagnosis using AI analysis of chest X-rays – GlobeNewswire

behold.ai and Wellbeing Software collaborate onnational solution for rapid COVID-19 diagnosis using AI analysis of chest X-rays

Companies working to fast-track programme for UK-wide rollout

LONDON, UK, March 31, 2020 Two British companies at the leading edge of medical imaging technology are working together on a plan to fast-track the diagnosis of COVID-19 in NHS hospitals using artificial intelligence analysis of chest X-rays.

behold.ai has developed the artificial intelligence-based red dot algorithm which can identify within 30 seconds abnormalities in chest X-rays. Wellbeing Software operates Cris, the UKs most widely used Radiology Information System (RIS), which is installed in over 700 locations.

A national roll-out combining these two technologies would enable a large number of hospitals to quickly process the significant volume of X-rays, currently being used as the key diagnostic test for triage of COVID-19 patients, thereby speeding up diagnosis and easing pressure on the NHS at this critical time. This solution will also find significant utility in dealing with the backlog of cases that continue to mount, such as suspected cancer patients.

Simon Rasalingham, Chairman and CEO of behold.ai, said:

behold.ai and Wellbeing are a great fit in terms of expertise and technology. We are able to prioritise abnormal chest X-rays with greater than 90% accuracy and a 30-second turnaround. If that were translated into a busy hospitals coping with COVID-19, the benefits to healthcare systems are potentially enormous.

Chris Yeowart, Director at Wellbeing Software, said:

Our technology provides the integration between the algorithm and the hospitals radiology systems and working processes, addressing the technical challenges to clearing the way for accelerated national rollout. It is clear from talking to radiology departments that chest X-rays have become one of the primary diagnostic tools for COVID-19 in this country.




For further information, please contact:Consilium Strategic Communications Tel: +44(0)20 3709 5700 beholdai@consilium-comms.com

About behold.ai and radiology

behold.ai provides artificial intelligence, through its red dot cognitive computing platform, to radiology departments. This technology augments the expertise of radiologists to enable them to report with greater clinical accuracy, faster and more safely than they could before. This revolutionary combination helps to deliver greater performance in radiology reporting at a fraction of the price of outsourced reporting.

Radiology departments play an essential role in the diagnostic process; however, a consequence of fewer radiologists and a growing demand for images has left services stretched beyond capacity across many trusts, resulting in reporting delays - in some cases impacting cancer diagnosis. These service issues have been highlighted by the Care Quality Commission and the Royal College of Radiologists.

Our solution seamlessly integrates into local trust workflows augmenting clinical practice and delivering state-of-the-art, safe, Artificial Intelligence.

The behold.ai algorithm has been developed using more than 30,000 example images, all of which have been reviewed and reported by highly experienced consultant radiology clinicians in order to shape accurate decision making. The red dot prioritisation platform is capable of sorting images into normal and abnormal categories in less than 30 seconds post image acquisition.

About behold.ai and quality

Apart from its FDA clearance,behold.aiis also CE approved and is gaining further approval for a CE mark Class IIa certification.

In June 2019 the Company was awarded ISO 13485 QMS certification for an AI medical device the gold standard of quality certification.

About Wellbeing Software

Wellbeing Software is a leading healthcare technology provider with a presence in more than 75% of NHS organisations. The company has combined its extensive UK resources and unparalleled experience in its specialist divisions radiology, maternity, data management and electronic health records - to form Wellbeing Software, uniting their core businesses to enable customers to build on existing investments in IT as a way of delivering connected healthcare records and better patient care. Wellbeings ability to connect its specialist systems with other third-party software enables healthcare organisations to achieve key objectives, such as paperless working and the creation of complete electronic health records. Through their established footprint, specialist knowledge and significant development resources, the company is building the foundations for connectivity within NHS organisations and beyond.

Wellbeing media contact : Jenni Livesley, Context Public Relations, wellbeing@contextpr.co.uk

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behold.ai and Wellbeing Software collaborate on national solution for rapid COVID-19 diagnosis using AI analysis of chest X-rays - GlobeNewswire

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Futuristic Impacts of AI Over Businesses and Society – Analytics Insight

In the past decade, artificial intelligence (AI) has made it to mainstream society from academic journals. The technology has achieved numerous milestones when it comes to digital transformation across society including businesses, education, and healthcare as well. Today people can do the tasks which were not even possible ten years back.

The proportion of organizations using AI in some form rose from 10 percent in 2016 to 37 percent in 2019 and that figure is extremely likely to rise further in the coming year, according to Gartners 2019 CIO Agenda survey.

While the breakthroughs in surpassing human ability at human pursuits, such as chess, make headlines, AI has been a standard part of the industrial repertoire since at least the 1980s. Then production-rule or expert systems became a standard technology for checking circuit boards and detecting credit card fraud. Similarly, machine-learning (ML) strategies like genetic algorithms have long been used for intractable computational problems, such as scheduling, and neural networks not only to model and understand human learning but also for basic industrial control and monitoring.

Moreover, AI is also the core of some of the most successful companies in history in terms of market capitalizationApple, Alphabet, Microsoft, and Amazon. Along with information and communication technology (ICT) more generally, the technology has revolutionized the ease with which people from all over the world can access knowledge, credit, and other benefits of a contemporary global society. Such access has helped lead to a massive reduction of global inequality and extreme poverty, for example by allowing farmers to know fair prices, the best crops, and giving them access to accurate weather predictions.

Following the trends, we can say that there will be big winners and losers as collaborative technologies, robots and artificial intelligence transform the nature of work. Moreover, data expertise will become exponentially more important. Across various organizations, the role of a senior manager in a deeply data-driven world is about to shift, thanks to the AI revolution. It is estimated that information hoarders will slow the pace of their organizations and forsake the power of artificial intelligence while competitors exploit it.

In the future, judgments about consumers and potential consumers will be made instantaneously and many organizations will put cybersecurity on par with other intelligence and defense priorities. Besides, open-source information and artificial intelligence collection will provide opportunities for global technological parity and soon predictive analytics and artificial intelligence could play an even more fundamental role in content creation.

With the growth of AI-enabled technologies in the future, societies will face challenges in realizing technologies that benefit humanity instead of destroying and intruding on the human rights of privacy and freedom of access to information. Also, the surging capabilities of robots and artificial intelligence will see a range of current jobs supplanted, where professional roles such as doctors, lawyers, and accountants could be replaced by artificial intelligence by the year 2025.

Moreover, low-skill workers will reallocate to tasks that are non-susceptible to computerization. All the risks will arise out of human activity from certain technological development in this technology, synthetic biology, nano techno, and artificial intelligence.

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Smriti is a Content Analyst at Analytics Insight. She writes Tech/Business articles for Analytics Insight. Her creative work can be confirmed @analyticsinsight.net. She adores crushing over books, crafts, creative works and people, movies and music from eternity!!

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